{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7baac2b8-739e-49c5-a3f2-9b9cf952d949",
   "metadata": {},
   "source": [
    "Chapter 33\n",
    "# K均值聚类\n",
    "Book_1《编程不难》 | 鸢尾花书：从加减乘除到机器学习  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1634cc98-ba1a-4157-a395-84607a4d6fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a7311ec-1ae8-494f-b687-0e316de310e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入并整理数据\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dd01b66-6014-44d4-8495-6a7080c94b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成网格化数据\n",
    "x1_array = np.linspace(4,8,101)\n",
    "x2_array = np.linspace(1,5,101)\n",
    "xx1, xx2 = np.meshgrid(x1_array,x2_array)\n",
    "# 创建色谱\n",
    "rgb = [[255, 238, 255],  \n",
    "       [219, 238, 244],  \n",
    "       [228, 228, 228]]  \n",
    "rgb = np.array(rgb)/255.\n",
    "cmap_light = ListedColormap(rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1cda148-d47b-4f9f-9098-d6c93e6cc1c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 采用KMeans聚类\n",
    "kmeans = KMeans(n_clusters=3, n_init = 'auto')\n",
    "cluster_labels = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a25feff-6ea6-4c53-8ebd-efe085961ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 预测聚类\n",
    "Z = kmeans.predict(np.c_[xx1.ravel(), xx2.ravel()])\n",
    "Z = Z.reshape(xx1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0c6c14c-1b83-4702-80e0-954a3d58d7b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.contourf(xx1, xx2, Z, cmap=cmap_light)\n",
    "ax.scatter(x=X[:, 0], y=X[:, 1], \n",
    "           color=np.array([0, 68, 138])/255., \n",
    "           alpha=1.0,\n",
    "           linewidth = 1, edgecolor=[1,1,1])\n",
    "# 绘制决策边界\n",
    "levels = np.unique(Z).tolist();\n",
    "ax.contour(xx1, xx2, Z, levels=levels,colors='r')\n",
    "centroids = kmeans.cluster_centers_\n",
    "ax.scatter(centroids[:, 0], centroids[:, 1], \n",
    "           marker=\"x\", s=100, linewidths=1.5,\n",
    "           color=\"r\")\n",
    "\n",
    "ax.set_xlim(4, 8); ax.set_ylim(1, 5)\n",
    "ax.set_xlabel(iris.feature_names[0])\n",
    "ax.set_ylabel(iris.feature_names[1])\n",
    "ax.grid(linestyle='--', linewidth=0.25, \n",
    "        color=[0.5,0.5,0.5])\n",
    "ax.set_aspect('equal', adjustable='box')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
